{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6QM5NKROLVYWYMZ7R2JOO2FV55","short_pith_number":"pith:6QM5NKRO","canonical_record":{"source":{"id":"1706.01581","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-06T01:56:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5a23baddcb337dd41b28ecab4b7a5bb5be6c91be52a058130fb50022a32633b4","abstract_canon_sha256":"3a8ac7e7a7cc04f8ec58f95e708101a853bcc401bd01a18957ed584a097275b4"},"schema_version":"1.0"},"canonical_sha256":"f419d6aa2e5d716c333f8e92e768b5ef7c42ab6525272269ab75ecd244cca614","source":{"kind":"arxiv","id":"1706.01581","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.01581","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.01581v1","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01581","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"pith_short_12","alias_value":"6QM5NKROLVYW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6QM5NKROLVYWYMZ7","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6QM5NKRO","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6QM5NKROLVYWYMZ7R2JOO2FV55","target":"record","payload":{"canonical_record":{"source":{"id":"1706.01581","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-06T01:56:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5a23baddcb337dd41b28ecab4b7a5bb5be6c91be52a058130fb50022a32633b4","abstract_canon_sha256":"3a8ac7e7a7cc04f8ec58f95e708101a853bcc401bd01a18957ed584a097275b4"},"schema_version":"1.0"},"canonical_sha256":"f419d6aa2e5d716c333f8e92e768b5ef7c42ab6525272269ab75ecd244cca614","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:56.613076Z","signature_b64":"FrBppMlOlVZiJVfYND6iAMKh2hBpQHHUnH/ZgoaNHj37pUjfnmUeZ/41ga4kxDu3CrwMLJERaYzoGEVcvHxoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f419d6aa2e5d716c333f8e92e768b5ef7c42ab6525272269ab75ecd244cca614","last_reissued_at":"2026-05-18T00:42:56.612336Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:56.612336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.01581","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LhH4ElXvgle/5b1SA741lzKbUMVlHA8L4/pGZPQS2UqWioal3Lm+zyxVM3vaniDeWP9foHKNN6hfEO33BTZyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:04:26.130269Z"},"content_sha256":"5f0430f99a98fa37cd18b7b44c0f4ed0412517ab9664983496152755ccf97e0e","schema_version":"1.0","event_id":"sha256:5f0430f99a98fa37cd18b7b44c0f4ed0412517ab9664983496152755ccf97e0e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6QM5NKROLVYWYMZ7R2JOO2FV55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embedding Feature Selection for Large-scale Hierarchical Classification","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Azad Naik, Huzefa Rangwala","submitted_at":"2017-06-06T01:56:51Z","abstract_excerpt":"Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improvin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01581","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0kMDZJtU73ZAqw43s/db02EM/vJ1e7qdDZTkTAzgurA/JZzDlASfUa35UgVPcStUEus/2vpMD+OStZx7MlD9DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:04:26.130993Z"},"content_sha256":"7926e983a28df1354e42fd092c55da3b905553ed6894afed629bcd161948d7ed","schema_version":"1.0","event_id":"sha256:7926e983a28df1354e42fd092c55da3b905553ed6894afed629bcd161948d7ed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/bundle.json","state_url":"https://pith.science/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T20:04:26Z","links":{"resolver":"https://pith.science/pith/6QM5NKROLVYWYMZ7R2JOO2FV55","bundle":"https://pith.science/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/bundle.json","state":"https://pith.science/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6QM5NKROLVYWYMZ7R2JOO2FV55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6QM5NKROLVYWYMZ7R2JOO2FV55","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3a8ac7e7a7cc04f8ec58f95e708101a853bcc401bd01a18957ed584a097275b4","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-06T01:56:51Z","title_canon_sha256":"5a23baddcb337dd41b28ecab4b7a5bb5be6c91be52a058130fb50022a32633b4"},"schema_version":"1.0","source":{"id":"1706.01581","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.01581","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.01581v1","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01581","created_at":"2026-05-18T00:42:56Z"},{"alias_kind":"pith_short_12","alias_value":"6QM5NKROLVYW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6QM5NKROLVYWYMZ7","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6QM5NKRO","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:7926e983a28df1354e42fd092c55da3b905553ed6894afed629bcd161948d7ed","target":"graph","created_at":"2026-05-18T00:42:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improvin","authors_text":"Azad Naik, Huzefa Rangwala","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-06T01:56:51Z","title":"Embedding Feature Selection for Large-scale Hierarchical Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01581","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5f0430f99a98fa37cd18b7b44c0f4ed0412517ab9664983496152755ccf97e0e","target":"record","created_at":"2026-05-18T00:42:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3a8ac7e7a7cc04f8ec58f95e708101a853bcc401bd01a18957ed584a097275b4","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-06T01:56:51Z","title_canon_sha256":"5a23baddcb337dd41b28ecab4b7a5bb5be6c91be52a058130fb50022a32633b4"},"schema_version":"1.0","source":{"id":"1706.01581","kind":"arxiv","version":1}},"canonical_sha256":"f419d6aa2e5d716c333f8e92e768b5ef7c42ab6525272269ab75ecd244cca614","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f419d6aa2e5d716c333f8e92e768b5ef7c42ab6525272269ab75ecd244cca614","first_computed_at":"2026-05-18T00:42:56.612336Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:56.612336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FrBppMlOlVZiJVfYND6iAMKh2hBpQHHUnH/ZgoaNHj37pUjfnmUeZ/41ga4kxDu3CrwMLJERaYzoGEVcvHxoDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:56.613076Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.01581","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f0430f99a98fa37cd18b7b44c0f4ed0412517ab9664983496152755ccf97e0e","sha256:7926e983a28df1354e42fd092c55da3b905553ed6894afed629bcd161948d7ed"],"state_sha256":"e0e553ea74932e82a11978744e8522d1a23c339c71466e656c1faa7320261113"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbrmbYcVW1K8nG79vbbpl7wHEAJUci00yne12b9e4SmHWUZxlfrtNJIaourqdkAeBGez+e+0G042+cCe1bgyCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T20:04:26.135039Z","bundle_sha256":"ff26bcb481782433d0b73e2097f27ad0adfde07214226319cba7ef9f4c3c2b6d"}}